Software Platform App Data Consistency Verification
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Solution Overview
Problem
In software platforms that extend functionality through third-party apps, inconsistencies between data maintained by the platform and app data can erode user confidence, as discrepancies in metrics and information can occur, leading to trust issues and system reliability problems.
Innovation Solution
A computer-implemented method that receives input from third-party apps, maintains relevant metrics, compares them with app data, and triggers actions to synchronize or correct discrepancies, ensuring data consistency between the software platform and the app, including corrective actions like resynchronizing data or informing users of inconsistencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is maintained separately by both the software platform and third-party apps, then each system can independently manage its own data, but inconsistencies and mismatches between platform metrics and app data occur
Solution Approach 1:
The patent implements a feedback mechanism where the software platform continuously compares its maintained metrics with data obtained from third-party apps. When inconsistencies are detected, the system generates feedback signals that trigger corrective actions, such as notifying users of the discrepancies or initiating resynchronization processes. This closed-loop feedback ensures that independent data management does not lead to persistent inconsistencies.
Solution Approach 2:
The patent introduces an intermediary comparison mechanism that acts as a mediator between the software platform's internal metrics and the third-party app's data. This intermediary layer performs systematic comparisons and facilitates coordination without requiring direct integration or centralized control, allowing both systems to maintain independence while ensuring consistency through structured mediation.
2Reliability
If the software platform continuously monitors and compares data with third-party apps, then data consistency is improved, but system complexity and computational overhead increase
Solution Approach 1:
The patent implements partial monitoring by selectively comparing specific metrics and data points rather than continuously analyzing all possible data. The system performs comparisons based on predefined criteria and triggers actions only when inconsistencies are detected, rather than maintaining constant surveillance. This approach ensures data consistency while avoiding the complexity and overhead of exhaustive continuous monitoring.
Solution Approach 2:
The patent segments the data comparison process into distinct, modular components: metric collection, data obtaining, comparison logic, and corrective action triggering. Each segment handles specific aspects of the consistency verification, making the overall system more manageable and less complex. This modular segmentation allows the platform to maintain reliability without being overwhelmed by system complexity.
3Reliability
If discrepancies between platform metrics and app data are detected and corrected, then user trust is maintained, but additional processing time and resources are required
Solution Approach 1:
The patent performs preliminary comparisons of metrics and app data at scheduled intervals or upon specific events, rather than waiting for user reports or continuous real-time analysis. By proactively detecting discrepancies before they affect user experience, the system maintains user trust without requiring constant processing. The preliminary action approach balances timeliness with resource efficiency.
Solution Approach 2:
The patent implements self-service mechanisms where the system automatically detects and corrects certain types of discrepancies without requiring extensive manual intervention or complex processing. For example, when inconsistencies are detected, the system can automatically notify relevant parties or initiate predefined correction protocols, reducing the time and resources needed for manual verification and resolution while maintaining user trust.
Data Source
AI summary
Systems and methods are provided for identifying a mismatch between data maintained by third party apps that provide extended functionality to a software platform, and metrics generated in the software platform based on interactions with the third party apps. This can be used to trigger appropriate actions such as, for example, corrective actions to remedy the mismatch. As a result, data consistency between the software platform and the app may be monitored and maintained.